Abstract Image super-resolution methods based on diffusion models have achieved remarkable success, but they still suffer from two significant limitations.On the one hand, this algorithm requires a large number of denoising steps in the sampling process, which seriously limits the inference speed of the model.On the other hand, although the existing methods can generate M Sandals diverse and detailed samples, they tend to perform unsatisfactorily on fidelity metrics such as the peak signal-to-noise ratio (PSNR).To address these challenges, this paper proposes a Multi-Scale Adversarial Diffusion Network (MSADN) based on super-resolution.A time-dependent discriminator is introduced to model complex multimodal distributions, significantly improving the efficiency of single-step sampling.
A Multi-Scale Generation Guidance (MSGG) module is designed to assist the model in learning feature information at different scales from low-resolution images, thereby enhancing its feature representation capability.Furthermore, to mitigate blurring artifacts introduced during the denoising process, a high-frequency loss function is proposed, targeting the residuals of ARTHROMEND high-frequency features between images.This ensures that the predicted images exhibit more realistic texture details.Experimental results indicate that, compared with other diffusion-based super-resolution methods, our approach provides a faster inference speed and has superior performance on benchmark datasets.